Database Of Events Research Articles

How shareholders react to sustainable narratives about leading European energy companies? An event study using sentiment data from the global database for events, language and tone (GDELT)

ABSTRACT This study aims to investigate how the shareholders of leading European energy companies value sustainability narratives. It uses news from the Global Database of Events, Location, Language, and Tone (GDELT) and analyses the cumulative average abnormal returns (CAAR) and abnormal volatilities (AV), incorporating the event study methodology. A total of 279,546 big news items were used, and 4,026 event studies were conducted. The extensive analysis of data and the segmentation of the news by tone, type of energy generation and environmental consequences helps to understand shareholders’ investment decisions. This study found that the sustainability narrative significantly impacts shareholder value; however, this narrative’s interpretation has no consensus. The sustainability news about these companies moves the stock market upwards for some shareholders, while others do the contrary. These results are observed by comparing CAAR and AV. The results found by this article are crucial for regulators to push forward an effective ecological transition. It should be legislated so that there is a common shareholders’ narrative, discouraging highly polluting investments.

Revolutionary Insurgents or Conservative Reactionaries? National Liberation Army’s Transnational Expansion in Colombia and Venezuela

The objective of this article is to document the spatial diffusion of the National Liberation Army (ELN) in Colombia and Venezuela. Building on a comprehensive database of more than 2100 violent incidents collected by the Armed Conflict Location & Event Database (ACLED) project and the Global Terrorism Database (GTD), the paper examines whether the ELN has expanded or relocated into Venezuela following the peace accords with the Revolutionary Armed Forces of Colombia (FARC) in 2016. We find that the vast majority of events and fatalities involving the ELN are located in Colombia. Recent years, however, have seen a significant increase in armed actions by the ELN on Venezuelan soil. This expansion was facilitated by the ultimate demobilization of the FARC and the ongoing social, political, and economic implosion that has wracked Venezuela since 2014. The ELN is a typical case of diffusion by escalation marked by a growth in violent events and fatalities in both the country of origin of armed groups and their new destination.

Institutional Strengthening in a Receding Movement: The Trajectory ofPiqueteroOrganizations between 2003 and 2015

This article contributes to the current debate on social movements and popular politics in Latin America by exploring the trajectory of organizations within the unemployed workers movement in Argentina, also known as thepiqueteros. Analysts agree that the recent history of this movement is characterized by a pronounced decline due to closing political opportunities. I suggest that a shift in the level of analysis toward organizational dynamics indicates a more complex scenario. While the influence of the movement as a political actor has declined, some piquetero organizations have in some aspects strengthened. I argue that this strengthening took place not despite the post-2003 context, but rather because of it. I advocate for an alternative conceptualization of piquetero groups, emphasizing their immersion in a particular political context and culture rather than their separation from it. I use interviews, participant observation, databases of contentious events, and a diverse literature to sustain my claims.

Indonesia paleotsunami database as an effort to reduce the tsunami disasters in Indonesia

Indonesia’s tectonic activity is one of the most active in the world. These conditions make Indonesia prone to earthquakes, tsunamis, and volcanic eruptions. Tsunamis are large ocean waves that are formed through several processes such as underwater earthquakes, underwater landslides, volcanic activity, and asteroid impacts. Indonesia has been hit by various tsunami events. However, not all characteristic tsunami events are well known and recorded. Knowing the characteristics of a tsunami occurrence is very important to understand the frequency and intensity of tsunamis in the present. This research create a WebGIS database for paleotsunami in Indonesia. Tsunami incident data comes from literature related to tsunami events such as books, journals, reports, final projects, and others. Making WebGIS in this study will be developed using website programming, namely HTML, CSS, and JavaScript. The research results obtained 302 tsunami data. The period of tsunami in this research is from Late Miocene to the last tsunami in 2018. The WebGIS created presents a database of tsunami events in Indonesia, a dashboard of Indonesian tsunami statistics, and a tsunami learning educational feature. It is hoped that the results of this database will serve as a means of information for future paleotsunami research needs and as a means of education regarding the potential for tsunamis in Indonesia.

Lesson (un)replicated: Predicting levels of political violence in Afghan administrative units per month using ARFIMA and ICEWS data

Abstract The aim of the present article is to evaluate the use of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model in predicting spatially and temporally localized political violent events using the Integrated Crisis Early Warning System (ICEWS). The performance of the ARFIMA model is compared to that of a naïve model in reference to two common relevant hypotheses: the ARFIMA model would outperform a naïve model and the rate of outperformance would deteriorate the higher the level of spatial aggregation. This analytical strategy is used to predict political violent events in Afghanistan. The analysis consists of three parts. The first is a replication of Yonamine’s study for the period beginning in April 2010 and ending in March 2012. The second part compares the results to those of Yonamine. The comparison was used to assess the validity of the conclusions drawn in the original study, which was based on the Global Database of Events, Language, and Tone, for the implementation of this approach to ICEWS data. Building on the conclusions of this comparison, the third part uses Yonamine’s approach to predict violent events in Afghanistan over a significantly longer period of time (January 1995–August 2021). The conclusions provide an assessment of the utility of short-term localized forecasting.

Disasters and civilian victimization: Exploring the dynamic effect in Africa, 1997–2017

How do natural disasters affect the treatment of civilians by non-state actors? On the one hand, conflict literature argues that adverse shocks influence the strategic interplay between rebel groups and the government, potentially increasing the level of violence against civilians. On the other hand, sociological research on natural disasters argues that they increase social integration and cooperation and are thus expected to reduce the risk of violent behaviour. In this study, we contend that the dynamics of violence against civilians are different in the short term and long term after a disaster strikes. We argue that natural disasters lead to a decrease in violence against civilians perpetrated by rebel groups in the short run. However, over time this temporary improvement in social integration starts to decline, increasing the level of civilian victimization. To examine this potential temporal dynamic, we combine georeferenced information on one-sided violence (Armed Conflict Location & Event Data; ACLED) and data on disasters (Emergency Event Database; EM-DAT). Our results confirm the expected dynamic effects. This research has important implications for our understanding of how disasters can influence the relationship between civilians, rebel groups, and governments.

An alarm data mining method for operation and maintenance support network based on stream computing

Communication system failure will produce a large number of alarm data. If it cannot be mined on time, it will affect the normal operation of the whole system. Based on this, an alarm data mining method of operation and maintenance support network based on streaming computing is proposed. The discrete alarm data is transformed into alarm transactions suitable for mining by sliding time window algorithm, and the transaction weight value is calculated by analytic hierarchy process; Spark framework and MapReduce framework are adopted to process data in batch, parallel and real-time, the alarm data mining of operation and maintenance support network is realised by setting a reasonable alarm event database threshold. Comparative experimental results show that the proposed method occupies the smallest memory and the shortest running time, and the performance of alarm data mining is the best.

Политика санкций США на уровне исполнительной власти

The article deals with the institutional performance of the U.S. executive power on the policy of sanctions. The topic is much less reflected in the literature in comparison with “canonical” issues like sanctions efficiency or particular case-studies of sanctions implementation. The goal is to fill in the gap in the literature on the following research questions. How do the U.S. sanctions policy executive institutions work? What is an extant of their involvement into the policy of sanctions? What is the balance of power between them? Why are particular institutes more active in comparison with others, and does it mean greater influence in terms of decision-making and implementation? The hypothesis implies that the U.S. executive institutions compose an imbalanced system with a skew in favor of the Department of the Treasury. However, this skew is smoothed by functional exclusiveness of other institutions and the impact of their decisions on sanctioned persons and states. “Sanctions Events Database” is used to test this assumption. Events are classified in terms of their institutional initiator (Department of the Treasury, Department of State, etc.), target states or problems, types of action, etc. The data is taken from open sources. It reflects a “micro-level” of institutional performance, depicting its everyday practice. The analysis demonstrates that a majority of events are connected to the Department of the Treasury, which is most active in comparison with other institutions. The Department of Commerce initiates much less events, however, the coverage of affected persons under its restrictions may be comparable to the one of the Treasury. The Department of Justice also initiates much less events, but the consequences for targets are harder due to criminal enforcement functions of the Department. The article relies on the 2020 data-set. This data is enough to understand basic trends of the U.S. executive institutions performance related to the policy of sanctions. Still, more data is needed to avoid possible fluctuations related to particular years.

A Comparison of the Thermodynamic and Kinematic Features in Recent Blizzard and Near-Blizzard Events in Iowa

Abstract The purpose of this research is to determine the validity of the subjective, anecdotal rule that four isobars present across Iowa during a wintertime, snow-bearing, extratropical cyclone is sufficient to create the winds that attend a blizzard (the “Four Isobar Rule”). Using data from the National Centers for Environmental Information's online Storm Event Database, we identified 15 cases of blizzards in Iowa over the course of 10 years (1999-2009 plus one event in 2010). Subjective analyses of mean sea level pressure for those 15 events revealed an 87% success rate of having four isobars present corresponding with the Blizzard events. Conversely, we also analyzed 23 Near-Blizzard events and discovered that approximately 74% did not have four isobars present. Clearly, there is overlap between these types of events. Composite meteorological fields of both case sets were completed using the North America Regional Reanalysis datasets. The composite results are generally consistent with other recent findings on Iowa blizzards. The surface low is often fairly deep (<1000 hPa), occluded or nearly so, featuring a convergent, descending lower troposphere, with 850-hPa winds of 45 kt or greater, and a near-surface layer whose lapse rate is nearly dry adiabatic. The composites of storms in the Blizzard category are slightly weaker than in prior literature, but the composites of storms in the Near-Blizzard category fail to meet any of the criteria for a blizzard in the state of Iowa.

Quantification and Assessment of Global Terrestrial Water Storage Deficit Caused by Drought Using GRACE Satellite Data

A drought-induced water storage deficit index (D-WSDI) was proposed to quantify the response of GRACE-based terrestrial water storage change to meteorological drought and the impact of drought on water storage deficit. D-WSDI was defined as the normalized residual component of GRACE time series data after removing the long-term trend and seasonal components. The evaluation based on the Emergency Events Database (EM-DAT) showed that more than 90% of global drought events from 2002 to 2019 led to the water storage deficit, which can be detected by the proposed D-WSDI. The severity of the water storage deficit caused by drought increases with the extending drought duration. Average 73% of water storage deficit months at the global scale is related to precipitation shortages. The cumulative precipitation deficit in relatively short periods of less than 9 months can lead to the water storage deficit in low-latitude regions, whereas a longer time scale is required to lead to a water storage deficit in high-latitude regions. The negative monthly precipitation anomaly of about -20% can lead to the water storage deficit in high rainfall regions, while the negative precipitation anomaly can reach -80% in arid and semi-arid areas. D-WSDI holds the capability to quantify the water storage deficit caused by drought, especially in the regions with terrestrial water storage change influenced by the long-term trends in climate and anthropogenic activities, and can be used as an index of drought monitoring with similar or superior performance compared to some traditional drought indices.

SLAM CHARACTERISTICS OF A HIGH-SPEED WAVE PIERCING CATAMARAN IN IRREGULAR WAVES

Slam characteristics of a 112m INCAT wave piercing catamaran in a range of realistic irregular sea conditions are presented in this paper. Towing tank testing of a 2.5 m hydroelastic segmented catamaran model was used to gather a database of slam events in irregular seas. The model was instrumented to measure motions, centrebow surface pressures and forces, encountered wave elevations and wave elevations within the bow area tunnel arches. From these measurements characteristics of the vessel slamming behaviour are examined: in particular relative vertical velocity, centrebow immersion, archway wave elevations and slam load distributions. A total of 2,098 slam events were identified over 22 different conditions, each containing about 80 to 100 slam events.
 The data, although inherently scattered, shows that encounter wave frequency and significant wave height are important parameters with regard to centrebow slamming. Relative vertical velocity was found to be a poor indicator of slam magnitude and slams were found to occur before the centrebow arch tunnel was completely filled, supporting the application of a two-dimensional filling height parameter as a slam indicator.

A Coastal Flood Event Database for the Southeastern Georgia and Southeastern South Carolina Coast and the Operational Implementation of a Tide Forecast Tool

Coastal flooding occurs when saltwater inundates normally dry land and the resulting impacts can range from minor flooding of low-lying areas along the coast, to significant damage to property and structures. Previous research consistently suggests that if sea-level rise continues to increase along the East Coast of the United States, coastal flooding will occur more frequently. In order to document the history of coastal flooding along the southeastern Georgia and southeastern South Carolina coast, a coastal flood event database was created for National Ocean Service tide gauges located in Charleston Harbor, South Carolina and Fort Pulaski, Georgia. Trends from the data show that coastal flooding is occurring more frequently with time at both tide gauges, particularly over the last five to ten years. Because of the increased frequency and worsening impacts of tidal flooding, a tide forecast tool is implemented operationally in an effort to improve deterministic tide forecasts. This study extends the dataset used in the Charleston Harbor forecast tool, expands the tool to Fort Pulaski, and compares the synoptic category forecast equations to an all-inclusive equation that does not differentiate by synoptic category. Results show that there is virtually no difference in the forecast accuracy between the all-inclusive forecast equation and the specific forecast equations based on synoptic category. Furthermore, the all-inclusive forecast equation can be implemented operationally, will help improve deterministic tide forecasts, and will likely aid in the decision-making process for Coastal Flood Watches, Warnings, and Advisories issued by the National Weather Service office in Charleston, South Carolina.

Political instability and households’ investment behavior: Evidence from Burkina Faso

There is a large, rather macroeconomic, literature that shows that political instability and social conflict are associated with poor economic outcomes including lower investment and reduced economic growth. However, there is only very little research on the impact of instability on households’ behavior, in particular their saving and investment decisions. We merge six rounds of household survey data and a geo-referenced time series of politically motivated events and fatalities from the Armed Conflict Location and Event Data Base (ACLED) to analyze households’ decisions when exposed to instability in Burkina Faso. For identification, we exploit variation in the intensity of political instability across time and space while controlling for time- and municipality-fixed effects as well as rainfall and nighttime light intensity, and many other potential confounders. Our results show a negative association of political instability and financial savings, the accumulation of durables, investment in house improvements, as well as investment in education and health. Instability seems, in particular, to lead to a reshuffling from investment expenditures to increased food consumption, implying lower growth prospects in the future. With respect to economic growth, the sizable education and health effects seem to be particularly worrisome.

Analysis of anticholinergic adverse effects using two large databases: The US Food and Drug Administration Adverse Event Reporting System database and the Japanese Adverse Drug Event Report database.

IntroductionAnticholinergic adverse effects (AEs) are a problem for elderly people. This study aimed to answer the following questions. First, is an analysis of anticholinergic AEs using spontaneous adverse drug event databases possible? Second, what is the main drug suspected of inducing anticholinergic AEs in the databases? Third, do database differences yield different results?MethodsWe used two databases: the US Food and Drug Administration Adverse Event Reporting System database (FAERS) and the Japanese Adverse Drug Event Report database (JADER) recorded from 2004 to 2020. We defined three types of anticholinergic AEs: central nervous system (CNS) AEs, peripheral nervous system (PNS) AEs, and a combination of these AEs. We counted the number of cases and evaluated the ratio of drug–anticholinergic AE pairs between FAERS and JADER. We computed reporting odds ratios (RORs) and assessed the drugs using Beers Criteria®.ResultsConstipation was the most reported AE in FAERS. The ratio of drug–anticholinergic AE pairs was statistically significantly larger in FAERS than JADER. Overactive bladder agents were suspected drugs common to both databases. Other drugs differed between the two databases. CNS AEs were associated with antidementia drugs in FAERS and opioids in JADER. In the assessment using Beers Criteria®, signals were detected for almost all drugs. Between the two databases, a significantly higher positive correlation was observed for PNS AEs (correlation coefficient 0.85, P = 0.0001). The ROR was significantly greater in JADER.ConclusionsThere are many methods to investigate AEs. This study shows that the analysis of anticholinergic AEs using spontaneous adverse drug event databases is possible. From this analysis, various suspected drugs were detected. In particular, FAERS had many cases. The differences in the results between the two databases may reflect differences in the reporting countries. Further study of the relationship between drugs and CNS AEs should be conducted.

Kenyan Women Bearing the Cost of Climate Change.

Climate change-induced crises can aggravate intimate partner violence (IPV); the loss of income when weather affects the agricultural industry can exacerbate violence at home. In Kenya, climate change has increased precipitation during the rainy season and raised temperatures during the dry season, resulting in floods and droughts. For 75% of Kenyans, agricultural activities are their primary source of income. This research aims to assess patterns in IPV and severe weather events (SWE). We examined Integrated Public Use Microdata Series-Demographic Health Survey (IPUMS-DHS) data from 2008 and 2014 for IPV severity and frequency. We used Emergency Events Database (EM-DAT) data along with GPS coordinates to identify SWEs (defined as any flood >10 days) by county in Kenya. Overall, women were more likely to experience IPV if their spouse worked in agriculture (Odds Ratio (OR) = 1.22, 95% Confidence Interval (CI): 1.10–1.36). There was a 60% increase in the odds of reporting IPV in counties that experienced an SWE as compared to counties that did not experience an SWE (OR = 1.60, 95% CI: 1.35–1.89). This analysis further supports the growing body of research that suggests a relationship between climate change-related weather events and violence against women.

On the relationship of power lines outages caused by thunderstorms with Forbush decreases of cosmic rays

It is known that power lines outages often occur during thunderstorms. Here are the results of comparing of power lines outages in Yakutia from 2012 to 2018 with the database of Forbush-Storm events. This database contains information on geomagnetic storms and Forbush-decreases of cosmic rays from 1996 to 2018. There are 3 classes of the events: if these two ground-based manifestations of solar wind disturbances occur simultaneously (Forbush with Storm, F+S) or separately (Forbush without Storm, F-S and Storm without Forbush, S-F). For 7 years in the summer time, 73 power lines outages associated with thunderstorms were recorded. It is shown that in 56 cases these outages occurred simultaneously with (F-S) class, 16 – with (F+S) class, and only in 1 case lightning outages were not associated with Forbush-Storm events (-F-S). In 19 cases of (S-F) class, not a single lightning outage was recorded. This means that lightning outages on power lines are mainly associated with decreases in the cosmic rays intensity, and during geomagnetic storms, power transmission disruptions occur when storms are simultaneous with Forbush-decreases of cosmic rays. Apparently, this indicates the significance of the effect of cosmic rays on atmospheric electricity, and it is more significant than the effect of geomagnetic storms.

Compound climate events increase tree drought mortality across European forests

Climate change can lead to the simultaneous occurrence of extreme droughts and heat waves increasing the frequency of compound events with unknown impacts on forests. Here we use two independent datasets, a compiled database of tree drought mortality events and the ICP-Forest level I plots, to study the impacts of the simultaneous occurrence of hot summers, with elevated vapour pressure deficit (VPD), and dry years on forest defoliation and mortality across Europe. We focused on tree drought mortality and background mortality rates, and we studied their co-occurrence with compound events of hot summers and dry years. In total, 143 out of 310 mortality events across Europe, i.e. 46% of cases, corresponded with rare compound events characterized by hot summers and dry years. Over the past decades, summer temperature increased in most sites and severe droughts resulted in compound events not observed before the 1980s. From the ICP-Forest plots we identified 291 (1718 trees) and 61 plots (128 trees) where severe defoliation and mortality, respectively, were caused by drought. The analyses of these events showed that 34% and 27% of the defoliation and mortality cases corresponded with rare compound climate events, respectively. Background mortality rates across Europe in the period 1993–2013 presented higher values in regions where summer temperature and VPD more steeply rose, where drought frequency increased. The steady increase in summer temperatures and VPD in Southern and Eastern Europe may favor the occurrence of compound events of hot summers and dry conditions. Giving that both, local and intense tree drought mortality events and background forest mortality rates, are linked to such compound events we can expect an increase in forest drought mortality in these European regions over the next decades.

Project of Counteracting Catastrophic Developments at Nature-Man-Made Objects in Russia

The problem-oriented project to counteract the development of catastrophes (henceforth the CDC Project) was set up as a particular response to two factors:Since the late 1970s, a rapidly advancing worldwide pandemic of technological accidents (catastrophes), highly costly in terms of their consequences, has been emerging (see the data in the Emergency Events Database (EM-DAT) [1] of the Centre for Research on the Epidemiology of Disasters (CRED) and the United Nations International Strategy for Disaster Reduction (UNISDR).There are no alternatives to the innovative evolution of Russia, with the key role played by systemic technologies [1].At this point, the problem-oriented groundwork has been laid in Russia for many years, including operational prototypes of catastrophe protection systems. The large-scale and fundamental works completed so far within the CDC Project attest to the perception of the critical demand for it. The Project is based on a systemic approach and a new technology of the problem-oriented innovative professional instrumentarium of research on nature-man-made objects (NMMOs). To date, the design of the CDC Project has been practically completed. Moreover, the Project has passed several tiers of critical review analysis at higher authorities, with relevant positive opinions offered in every case. Under the circumstances (just before further rollout of works under the CDC Project), a decision was made to advise appropriate publication of a series of thematically related articles on academic and technological issues, this paper being the first of them.

Better, Faster, Stronger: Using Machine Learning to Analyse South African Police-recorded Protest Data

ABSTRACT A long-important tool for quantitative analysis of protests, the potential power of Protest Event Analysis (PEA) has only increased with the rise of Machine Learning technologies and the ubiquity of big data. PEA coders also present an advantage over contemporary Natural Language Programming innovations by being customisable to incorporate locally appropriate terms and vernaculars, expressed as personalised ontologies. As such, there is a need to develop a standard process for deploying machine learning tools that can draw on the local. This paper introduces such a tool, innovating the numeration of abstract indicators. “Machine Learning Protest Event Analysis Keyword Enumerated Recoding” is a protocol that enables PEA coders to read and classify large “event databases”, incorporating local terms and abstract indicators into the analysis. Applying this protocol to 150,000 records in a police-recorded database of crowd events in South Africa, protest events could be individually rated by levels of “tumult”—a feat hitherto inhibited by conventional PEA methods. Innovations in estimating crowd sizes, as well as an updated view of post-apartheid protest, showing that protests tend to be more common but less prone to violence than previous theories concluded, speaks to the potential for this protocol to unearth novel insights on even bigger data sets.

Machine Learning Based Identification of Microseismic Signals Using Characteristic Parameters.

Microseismic monitoring system is one of the effective means to monitor ground stress in deep mines. The accuracy and speed of microseismic signal identification directly affect the stability analysis in rock engineering. At present, manual identification, which heavily relies on manual experience, is widely used to classify microseismic events and blasts in the mines. To realize intelligent and accurate identification of microseismic events and blasts, a microseismic signal identification system based on machine learning was established in this work. The discrimination of microseismic events and blasts was established based on the machine learning framework. The microseismic monitoring data was used to optimize the parameters and validate the classification methods. Subsequently, ten machine learning algorithms were used as the preliminary algorithms of the learning layer, including the Decision Tree, Random Forest, Logistic Regression, SVM, KNN, GBDT, Naive Bayes, Bagging, AdaBoost, and MLP. Then, training set and test set, accounting for 50% of each data set, were prospectively examined, and the ACC, PPV, SEN, NPV, SPE, FAR and ROC curves were used as evaluation indexes. Finally, the performances of these machine learning algorithms in microseismic signal identification were evaluated with cross-validation methods. The results showed that the Logistic Regression classifier had the best performance in parameter identification, and the accuracy of cross-validation can reach more than 0.95. Random Forest, Decision Tree, and Naive Bayes also performed well in this data set. There were some differences in the accuracy of different classifiers in the training set, test set, and all data sets. To improve the accuracy of signal identification, the database of microseismic events and blasts should be expanded, to avoid the inaccurate data distribution caused by the small training set. Artificial intelligence identification methods, including Random Forest, Logistic Regression, Decision Tree, Naive Bayes, and AdaBoost algorithms, were applied to signal identification of the microseismic monitoring system in mines, and the identification results were consistent with the actual situation. In this way, the confusion caused by manual classification between microseismic events and blasts based on the characteristics of waveform signals is solved, and the required source parameters are easily obtained, which can ensure the accuracy and timeliness of microseismic events and blasts identification.